{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def get_exact_samples(desired_mean, desired_std_dev) :\n",
    "    num_samples = 2\n",
    "    samples = np.random.normal(loc=0.0, scale=desired_std_dev, size=num_samples)\n",
    "\n",
    "    actual_mean = np.mean(samples)\n",
    "    actual_std = np.std(samples)\n",
    "\n",
    "    zero_mean_samples = samples - (actual_mean)\n",
    "\n",
    "    zero_mean_mean = np.mean(zero_mean_samples)\n",
    "    zero_mean_std = np.std(zero_mean_samples)\n",
    "\n",
    "    scaled_samples = zero_mean_samples * (desired_std_dev/zero_mean_std)\n",
    "    scaled_mean = np.mean(scaled_samples)\n",
    "    scaled_std = np.std(scaled_samples)\n",
    "\n",
    "    final_samples = scaled_samples + desired_mean\n",
    "    final_mean = np.mean(final_samples)\n",
    "    final_std = np.std(final_samples)\n",
    "#     print(\"Final samples stats     : mean = {:.4f} stdv = {:.4f}\".format(final_mean - desired_mean, final_std - desired_std_dev))\n",
    "    return final_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Trainers.DatasetBC import *\n",
    "from ExperimentsBC import *\n",
    "from common_code.plotting import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_names = {\n",
    "    'sst' : 'SST',\n",
    "    'imdb' : 'IMDB',\n",
    "    'agnews' : 'AG News',\n",
    "    'tweet' : 'ADR',\n",
    "    'snli' : 'SNLI',\n",
    "    '20News_sports' : '20 News Sports',\n",
    "    'diab' : 'Diabetes',\n",
    "    'cnn' : 'CNN',\n",
    "    'anemia' : 'Anemia',\n",
    "    'babi_1' : 'bAbI 1',\n",
    "    'babi_2' : 'bAbI 2',\n",
    "    'babi_3' : 'bAbI 3'\n",
    "}  \n",
    "\n",
    "dataset_order = list(map(lambda x : dataset_names[x], \n",
    "                      ['sst', 'imdb', 'tweet', 'agnews', '20News_sports', \n",
    "                       'diab', 'anemia', 'cnn', 'babi_1', 'babi_2', 'babi_3', 'snli']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 72,
       "width": 296
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [\"mahogany\", \"amber\", \"greyish\", \"faded green\", \"dusty purple\"]\n",
    "sns.palplot(sns.xkcd_palette(colors))\n",
    "sns.set_palette(sns.xkcd_palette(colors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 921.6x633.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 558,
       "width": 945
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = 'lstm+tanh'\n",
    "model_1 = 'average+tanh'\n",
    "import os\n",
    "models = [x for x in os.listdir('graph_outputs/GradientPval_kendalltau/') if model in x]\n",
    "import pandas as pd\n",
    "dfs = []\n",
    "for d in models :\n",
    "    df = pd.read_csv('graph_outputs/GradientPval_kendalltau/' + d, index_col=0)\n",
    "    df['Model'] = 'lstm'\n",
    "    x = d.split('+')[0]\n",
    "    df_1 = pd.read_csv('graph_outputs/GradientPval_kendalltau/' + x + '+' + model_1 + '.csv', index_col=0)\n",
    "    df_1['Model'] = 'average'\n",
    "    if x in dataset_names :\n",
    "        df['dataset'] = x\n",
    "        df_1['dataset'] = x\n",
    "        data_1 = get_exact_samples(df.loc['Overall']['mean'], df.loc['Overall']['std'])\n",
    "        for s in data_1 :\n",
    "            dfs.append({'dataset' : x, 'Model' : 'lstm', 'val' : s})\n",
    "        data_1 = get_exact_samples(df_1.loc['Overall']['mean'], df_1.loc['Overall']['std'])\n",
    "        for s in data_1 :\n",
    "            dfs.append({'dataset' : x, 'Model' : 'average', 'val' : s})\n",
    "\n",
    "import seaborn as sns\n",
    "dfs = pd.DataFrame(dfs)\n",
    "dfs['dataset'] = dfs.dataset.apply(lambda x : dataset_names[x])\n",
    "fig = sns.barplot(y='dataset', x='val', order=dataset_order, hue='Model', data=dfs, ci='sd', errwidth=2, errcolor='#999999')\n",
    "xmin, xmax = fig.axes.get_ylim()\n",
    "# fig.axes.vlines(dfs['val'].mean(), xmin, xmax, colors='#222222')\n",
    "sns.despine()\n",
    "plt.xlabel(\"Mean Kendall-$\\\\tau$\", fontsize=25)\n",
    "plt.ylabel(\"Dataset\", fontsize=25)\n",
    "plt.ylim(xmin, xmax)\n",
    "plt.xticks(fontsize=22)\n",
    "plt.yticks(fontsize=22)\n",
    "fig.axes.get_legend().remove()\n",
    "# plt.gcf().set_size_inches(15, 15)\n",
    "plt.savefig('GRes.svg', bbox_inches='tight', pad_inches=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 921.6x633.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 558,
       "width": 945
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = 'lstm+tanh'\n",
    "model_1 = 'average+tanh'\n",
    "import os\n",
    "dirname = 'graph_outputs/pyxc-pyc_pval_kendalltau/'\n",
    "models = [x for x in os.listdir(dirname) if model in x]\n",
    "import pandas as pd\n",
    "dfs = []\n",
    "for d in models :\n",
    "    df = pd.read_csv(dirname + d, index_col=0)\n",
    "    df['Model'] = 'lstm'\n",
    "    x = d.split('+')[0]\n",
    "    df_1 = pd.read_csv(dirname + x + '+' + model_1 + '.csv', index_col=0)\n",
    "    df_1['Model'] = 'average'\n",
    "    if x in dataset_names :\n",
    "        df['dataset'] = x\n",
    "        df_1['dataset'] = x\n",
    "        data_1 = get_exact_samples(df.loc['Overall']['mean'], df.loc['Overall']['std'])\n",
    "        for s in data_1 :\n",
    "            dfs.append({'dataset' : x, 'Model' : 'lstm', 'val' : s})\n",
    "        data_1 = get_exact_samples(df_1.loc['Overall']['mean'], df_1.loc['Overall']['std'])\n",
    "        for s in data_1 :\n",
    "            dfs.append({'dataset' : x, 'Model' : 'average', 'val' : s})\n",
    "\n",
    "import seaborn as sns\n",
    "dfs = pd.DataFrame(dfs)\n",
    "dfs['dataset'] = dfs.dataset.apply(lambda x : dataset_names[x])\n",
    "fig = sns.barplot(y='dataset', x='val', order=dataset_order, hue='Model', data=dfs, ci='sd', errwidth=2, errcolor='#999999')\n",
    "xmin, xmax = fig.axes.get_ylim()\n",
    "# fig.axes.vlines(dfs['val'].mean(), xmin, xmax, colors='#222222')\n",
    "sns.despine()\n",
    "plt.xlabel(\"Mean Kendall-$\\\\tau$\", fontsize=25)\n",
    "plt.ylabel(\"Dataset\", fontsize=25)\n",
    "plt.ylim(xmin, xmax)\n",
    "plt.xticks(fontsize=22)\n",
    "plt.yticks(fontsize=22)\n",
    "fig.axes.get_legend().remove()\n",
    "# plt.gcf().set_size_inches(15, 15)\n",
    "# plt.savefig('graph_outputs/CorrGL-AG_summary.pdf', bbox_inches='tight', pad_inches=0)\n",
    "plt.savefig('LRes.svg', bbox_inches='tight', pad_inches=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = 'lstm+tanh'\n",
    "import os\n",
    "models = [x for x in os.listdir('graph_outputs/CorrGL-AG_kendalltau/') if model in x]\n",
    "import pandas as pd\n",
    "dfs = {}\n",
    "for d in models :\n",
    "    df = pd.read_csv('graph_outputs/CorrGL-AG_kendalltau/' + d, index_col=0)\n",
    "    dfs[d.split('+')[0]] = df.loc['Overall']\n",
    "\n",
    "import seaborn as sns\n",
    "dfs = pd.DataFrame(dfs).transpose()\n",
    "dfs.index = dfs.index.map(lambda x : dataset_names[x])\n",
    "fig = sns.barplot(y=dfs.index, x=dfs['mean'], order=dataset_order)\n",
    "\n",
    "xmin, xmax = fig.axes.get_ylim()\n",
    "fig.axes.vlines(dfs['mean'].mean(), xmin, xmax, colors='#222222')\n",
    "sns.despine()\n",
    "plt.xlabel(\"Mean Difference between Correlations\", fontsize=25)\n",
    "plt.ylabel(\"Dataset\", fontsize=25)\n",
    "plt.ylim(xmin, xmax)\n",
    "plt.xticks(fontsize=22)\n",
    "plt.yticks(fontsize=22)\n",
    "# plt.gcf().set_size_inches(15, 15)\n",
    "plt.savefig('graph_outputs/CorrGL-AG_summary.pdf', bbox_inches='tight', pad_inches=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "models = [x for x in os.listdir('graph_outputs/CorrStats_kendalltau/') if 'lstm+tanh' in x]\n",
    "models_avg = [x for x in os.listdir('graph_outputs/CorrStats_kendalltau/') if 'average+tanh' in x]\n",
    "\n",
    "import pandas as pd\n",
    "dfs = {}\n",
    "for d, d1 in zip(models, models_avg) :\n",
    "    df = pd.read_csv('graph_outputs/CorrStats_kendalltau/' + d, index_col=0).rename(columns=lambda x : x + '_lstm')\n",
    "    df_1 = pd.read_csv('graph_outputs/CorrStats_kendalltau/' + d1, index_col=0).rename(columns=lambda x : x + '_avg')\n",
    "    df = pd.concat([df, df_1], axis=1)\n",
    "    dfs[d.split('+')[0]] = df.loc['ag']\n",
    "\n",
    "import seaborn as sns\n",
    "dfs = pd.DataFrame(dfs).transpose()\n",
    "dfs.index = dfs.index.map(lambda x : dataset_names[x])\n",
    "fig = sns.barplot(y=dfs.index, x=dfs['mean_avg'] - dfs['mean_lstm'], order=dataset_order)\n",
    "\n",
    "xmin, xmax = fig.axes.get_ylim()\n",
    "fig.axes.vlines((dfs['mean_avg'] - dfs['mean_lstm']).mean(), xmin + 0.5, xmax - 0.5, colors='#222222')\n",
    "sns.despine()\n",
    "plt.xlabel(\"Mean Difference between Correlations\", fontsize=25)\n",
    "plt.ylabel(\"Dataset\", fontsize=25)\n",
    "plt.ylim(xmin, xmax)\n",
    "plt.xticks(fontsize=22)\n",
    "plt.yticks(fontsize=22)\n",
    "# plt.savefig('graph_outputs/CorrAL(avg-lstm)_summary.pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Trainers.DatasetQA import *\n",
    "from ExperimentsQA import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in datasets :\n",
    "    print(k)\n",
    "    dataset = datasets[k]()\n",
    "    dataset.basepath = 'outputs_dev'\n",
    "    generate_graphs_on_encoders(dataset, ['cnn', 'lstm', 'average', 'cnn_dot', 'lstm_dot', 'average_dot'])\n",
    "    print('+'*700)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = datasets_ehr['mortality']()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Trainers.PlottingBC import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluator = run_evaluator_on_latest_model(dataset, config='lstm')\n",
    "logodds_results = pload(evaluator.model, 'logodds_attention')\n",
    "emax_jds, emax_adv_attn, emax_ad_y = plot_attn_diff(dataset, dataset.test_data, logodds_results, \n",
    "                                                    save_name='logodds_subs', dirname=evaluator.model.dirname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 308\n",
    "a = print_adversarial_example(dataset.vec.map2words(dataset.test_data.X[n]), dataset.test_data.attn_hat[n], emax_adv_attn[n], latex=True)\n",
    "dataset.test_data.yt_hat[n], emax_ad_y[n]\n",
    "print(a[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ent = [np.max(x) for x in emax_adv_attn]\n",
    "plt.scatter(ent, emax_jds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_1 = set(np.where(np.array(ent) > 0.6)[0]) & set(np.where(np.array(emax_jds) > 0.4)[0])\n",
    "idx_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset(dataset, 'logodds_lstm_reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_logodds_experiment(dataset, 'logodds_lstm_reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_experiments_on_latest_model(dataset, 'logodds_lstm_reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "generate_graphs_on_latest_model(dataset, 'logodds_lstm_reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_logodds_substitution_experiment(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = dataset.test_data\n",
    "plt.scatter(test_data.yt_hat[:, 0], test_data.opp_yt_hat[:, 0], s=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff = np.abs(test_data.yt_hat[:, 0] - test_data.opp_yt_hat[:, 0])\n",
    "np.argsort(diff)[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(diff, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = dataset.test_data\n",
    "for k, v in test_data.logodds_combined[0].items() :\n",
    "    print(dataset.vec.map2words(v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 4231\n",
    "true_X = dataset.vec.map2words(test_data.X[n])\n",
    "new_X = dataset.vec.map2words(test_data.opp_X[n])\n",
    "print_attn(true_X, dataset.test_data.attn_hat[n])\n",
    "print(test_data.yt_hat[n])\n",
    "print_attn(new_X, dataset.test_data.opp_attn[n])\n",
    "print(test_data.opp_yt_hat[n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in datasets_ehr :\n",
    "    dataset = datasets_ehr[k]()\n",
    "    generate_graphs_on_latest_model(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in datasets_ehr :\n",
    "    dataset = datasets_ehr[k]()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in datasets_ehr :\n",
    "    dataset = datasets_ehr[k]()\n",
    "    train_dataset(dataset, 'logodds_lstm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in datasets_ehr :\n",
    "    dataset = datasets_ehr[k]()\n",
    "    run_evaluator_on_latest_model(dataset, 'logodds_lstm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model.LR import LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in datasets_ehr :\n",
    "    if k != 'pheno' : continue\n",
    "    dataset = datasets_ehr[k]()\n",
    "    train_lr_on_dataset(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in datasets_ehr :\n",
    "    dataset = datasets_ehr[k]()\n",
    "    push_all_models(dataset, dataset.keys_to_use)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in datasets_ehr :\n",
    "    dataset = datasets_ehr[k]()\n",
    "    run_logodds_experiment(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list(enumerate(dataset.vec.label_headers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_top_words(dataset, config='lstm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array(dataset.test_data.yt_hat)\n",
    "idx_y = np.where(y > 0.8)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "top_words_dict = defaultdict(float)\n",
    "for i in idx_y :\n",
    "    d = dataset.test_data.top_words_attn[i]\n",
    "    for k, v in d.items() :\n",
    "        top_words_dict[k] += v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_words = dict(sorted(top_words_dict.items(), key=lambda x: x[1])[-20:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr.print_all_features(n=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_words_attn = set(top_words.keys())\n",
    "top_words_lr = set(lr.get_features(n=20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_words_attn & top_words_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(top_words_attn & top_words_lr) / len(top_words_attn | top_words_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.keys_to_use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multi Adversarial Examples\n",
    "=========================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in datasets_ehr :\n",
    "    dataset = datasets_ehr[k]()\n",
    "#     generate_adversarial_examples(dataset, config='lstm')\n",
    "#     generate_logodds_examples(dataset, config='lstm')\n",
    "#     generate_graphs_on_latest_model(dataset, config='lstm')\n",
    "    push_all_models(dataset, keys=dataset.keys_to_use)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.vec.label_headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in datasets_ehr :\n",
    "    dataset = datasets_ehr[k]()\n",
    "    dataset.display_stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(dataset.test_data.X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluator = run_evaluator_on_latest_model(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_adversarial_outputs = pload(evaluator.model, 'multi_adversarial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Trainers.PlottingBC import *\n",
    "test_data = dataset.test_data\n",
    "emax_jds, emax_adv_attn, emax_ad_y = plot_multi_adversarial(test_data.X, test_data.yt_hat, \n",
    "                                                            test_data.attn_hat, multi_adversarial_outputs, dirname=\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print_adversarial_examples(dataset, test_data.X, test_data.yt_hat, test_data.attn_hat, \n",
    "                           emax_jds, emax_adv_attn, emax_ad_y, by_class=None, dirname='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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